IMPROVING FAKE PRODUCT DETECTION USING AI-BASED TECHNOLOGY

Abstract

ResearchAndMarkets wrote in their report on May 15, 2018, that up to 1.2 Trillion USD in 2017 of products are counterfeited goods. The report estimated this damage globally at 1.82 Trillion USD in 2020 (RESEARCH AND MARKETS, 2018). This paper does not consider copyrights or digital piracy, counterfeiting, fraudulent documents but rather investigates the prevention of counterfeiting on a technological basis. The presence of counterfeit products on the European market is on the increase, therefore the intervention of inspection bodies and authorities alone is not sufficient, consumers can make their contribution and support this process. In this paper, we research the possibility to reduce counterfeit products using machine learning-based technology. Image and text recognition and classification based on machine learning have the potential to be a key technology in the fight against counterfeiting. The automatic image and text recognition and the classification of product information enable end customers to detect counterfeits precisely and quickly by checking them against trained models. The goal of this paper is to create an easy to use applications in which the end-user identifies the counterfeit product and contribute to the fight against product piracy.